{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特征工程：通过一系列的工程活动，将信息使用更高效的编码方式表示，并尽量减少原始数据中的不确定因素的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=pd.read_csv('F:\\\\CSDN\\\\pima-indians-diabetes.csv')\n",
    "train.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.info()\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "describe是需要注意的一个地方，缺失值是需要进行处理的，不然最后的结果会受到影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names=['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "print((train[NaN_col_names]==0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bacfa06400>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XkhwDXA+8BHg38OKqettC/jHSo3EFIR2630vydUZbQCwHXtDqPwKubMcnA9+squ/UaLuCj857/quAP0lyI3Ad8HTgpAXpXDoEriCkQ5DklcDLgTVV9YMkX2L0HzzAD+qRvWt626gz79y6qvrWPq/98ie8YelxcAUhHZqjgN0tHH4a+LlHmXcrow3mViQJ8Cvzzn0O+N29gyQva4cPAM8eoGfpMTEgpEPzz8Az2yWmCxndP9hPVT3EaJfcfwW+yOg7Nu5vp9/VXuPmJLcC72z1a4GXthvX3qTWxLmbqzSQJM+qqgfbCuJvgJur6uJJ9yWNyxWENJzfajeivwE8A/jbCfcjHRJXEJKkLlcQkqQuA0KS1GVASJK6DAhJUpcBIUnqMiAkSV3/D1k8lXUTrwc1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Target分布，看看样本分布是否均衡\n",
    "sns.countplot(train.Target)\n",
    "#在这个样本里面只有得病和没得病两种可能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bacfc98978>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#考虑每个特征的分布\n",
    "sns.countplot(train.pregnants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bad1182b00>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"pregnants\",hue=\"Target\",data=train)\n",
    "#data不能省，说明数据来源于哪里，hue表示一种细分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bad1386e48>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.Plasma_glucose_concentration,kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bad16dcf28>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=\"Target\",y=\"Plasma_glucose_concentration\",data=train,hue=\"Target\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bad18963c8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.blood_pressure,kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bad1947dd8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x=\"Target\",y=\"blood_pressure\",data=train,hue=\"Target\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bad1937940>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.Triceps_skin_fold_thickness,kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bad1a3cba8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target',y=\"Triceps_skin_fold_thickness\",data=train,hue=\"Target\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bad2afd240>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.serum_insulin,kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bad2bc4c88>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='serum_insulin', data=train, hue=\"Target\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bad2bb7630>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xR4AvAG8A944gnzQUTr9on1NV/wisB6bm7PsRsB24gcGqldJEcqSufU6Sk4H9GCyedNCch/4E+Luq+v5gAUtp8ljq2lfsmVOHwYdTbK6q9+aWd1U9h1e9aMK5SqMkNcQ5dUlqiKUuSQ2x1CWpIZa6JDXEUpekhljqktQQS12SGmKpS1JD/hcrTevRwP8/MQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.BMI,kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bad2ca60f0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='BMI', data=train, hue=\"Target\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bad2dc0400>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "BMIDF=train.groupby(['BMI','Target'])['BMI'].count().unstack('Target').fillna(0)\n",
    "BMIDF[[0,1]].plot(kind='bar',stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bad2d15be0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train.Diabetes_pedigree_function, kde = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2bad3502b38>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr=train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xR4AvAG8A944gnzQUTr9on1NV/wisB6bm7PsRsB24gcGqldJEcqSufU6Sk4H9GCyedNCch/4E+Luq+v5gAUtp8ljq2lfsmVOHwYdTbK6q9+aWd1U9h1e9aMK5SqMkNcQ5dUlqiKUuSQ2x1CWpIZa6JDXEUpekhljqktQQS12SGmKpS1JD/hcrTevRwP8/MQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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50bhrkUbBoJe6A3S+QHcQmNQcg15Htf5cSufTnS72ir7tGUk+0J9L/PYkdyS5rL/v5Un+tT8R2mfnDs2XJplBr6PdpXTncf8P4JEkZwG/C6wDfo3uaMbz4BfnXvoH4LKqejlwHXDNOIqWlmJkPw4uTYnNdCdmg+7Q9M3AM4FPVdXPge8luau//3TgTODO7vQlHEN3Clppohn0Omr151C5EDgzSdEFd/HEOXCe8hDg/qo67wiVKA2FUzc6ml0GfKyqXlBV66pqLd0vNT0M/F4/V38ycEHffw8wk+QXUzlJXjKOwqWlMOh1NNvMU7feb6L7oYhZutMWf5DujKmPVtXP6P44vCfJV4F76c4xLk00z14pLSDJc6rqx/30zt10v0b1vXHXJS2Hc/TSwm7vfyzmWcBfG/KaZm7RS1LjnKOXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9Jjfs/BtPpG+2qagEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for x in train.columns:\n",
    "    sns.distplot(train[x],kde=False)\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
